feat: 报告页 V2.2 检测可视化 + 评分引擎审计 + 真值回归/e2e 闭环
- server.js: face_maps 改为白名单 5 图层完整 dataURL 透传(REPORT_MAPS,按魔数判 mime),marks 坐标随 result 原样透传;实测 return_marks 不支持毛孔/黑头等扩展标记 - skin-score.js: 按需求文档 V2.2 逐字审计修正 9 处(黑眼圈 dark_circle_severity、oil_desc/spot_flag §7.2 拼接、skintone 中文映射、轮廓预警档前缀、痘痘 heavy_note、50+精华全文、综合文案数值格式),契约引用 V2.0→V2.2 - app.js/index.html/css: 报告页 6 分类检测可视化(轮廓=正脸实拍、皱纹=正脸压暗+皱纹增强线、光滑=正脸+色斑/痣/痤疮标记、肤色=色沉图、敏感=红区图、痘痘=正脸+闭口/痤疮标记),数据驱动 VIZ_CAT 配置,优雅降级 - h5/test/: Excel 两组三视图真值回归 84/84(含案例2 AY 列扁平化数据重建 fixture) - h5/assets/mock/: 真实案例1 报告页 fixture(坐标与照片同比例缩放),?mock=1&screen=report 直渲 - e2e: Chrome 无头闭环 55/55(mock 全流程/直渲/真实 API 降级链路),截图存档 preview/e2e-20260926 - 自测: skin-score 83/83,fixture 冒烟 35/35
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# ═════ 真值 fixture 构建脚本（零第三方新依赖：openpyxl 环境已装，仅用于建 fixture）═════
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# 用法：python3 h5/test/build_fixtures.py
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# 产出：
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# fixtures/case1.json ← reference/facepp-skinanalyze/examples/ref_3view_raw.json
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# （案例1 完整原始返回，精简坐标数组与 face_maps base64）
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# fixtures/case2.json ← docs/皮肤检测测试结果_三视图_20260926.xlsx AY4 扁平化文本解析回嵌套
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# fixtures/truth.json ← 同表第 3/4 行真值（P/S/档位/文案等断言目标）
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# 两个 case fixture 均为服务端 /api/skin-analyze 响应形状：
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# { result, face_rectangle, side: { left: left_side_result, right: right_side_result } }
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import json
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import os
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import re
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import sys
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import openpyxl
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ROOT = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..'))
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REF_JSON = os.path.join(ROOT, 'reference', 'facepp-skinanalyze', 'examples', 'ref_3view_raw.json')
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XLSX = os.path.join(ROOT, 'docs', '皮肤检测测试结果_三视图_20260926.xlsx')
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OUT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'fixtures')
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# ─── 通用：递归精简（坐标数组→[]，face_maps→{}；评分只用标量与 count）───
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def slim(node, key=''):
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if isinstance(node, dict):
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if key == 'face_maps': # base64 整图，评分不需要，整体置空对象
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return {}
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return {k: slim(v, k) for k, v in node.items()}
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if isinstance(node, list):
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# 元素为 dict 的坐标/标记数组（rectangle/polygon/*_mark/*_outline）→ 空数组；
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# 纯数值数组（confidence 列表等）保留
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if node and all(isinstance(x, dict) for x in node):
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return []
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return [slim(x, key) for x in node]
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return node
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# ─── 案例1：原始 pro 返回 → 服务端响应形状 ───
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def build_case1():
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with open(REF_JSON, encoding='utf-8') as f:
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raw = json.load(f)
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fixture = {
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'request_id': raw.get('request_id'),
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'time_used': raw.get('time_used'),
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'result': slim(raw['result']),
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'face_rectangle': raw.get('face_rectangle'),
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'side': {
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'left': slim(raw.get('left_side_result')),
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'right': slim(raw.get('right_side_result'))
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}
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}
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return fixture
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# ─── 案例2：AY4 扁平化「路径 = 值」文本 → 嵌套 JSON ───
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SEC_RE = re.compile(r'^——\s*(.+?)\s*——$')
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KV_RE = re.compile(r'^([^=]+?)\s*=\s*(.*)$')
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NUMLIST_RE = re.compile(r'^[\d\s,.\-eE+]+$')
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INT_RE = re.compile(r'^-?\d+$')
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FLOAT_RE = re.compile(r'^-?(?:\d+\.\d*|\.\d+)(?:[eE][-+]?\d+)?$|^-?\d+[eE][-+]?\d+$')
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MS_RE = re.compile(r'^(-?\d+(?:\.\d+)?)\s*ms$')
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skipped = [] # [摘要] 跳过的路径（坐标数组/base64/深层结构）
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def parse_value(path, s):
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s = s.strip()
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if s == '':
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return None, False
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if s.startswith('{'): # 行内 JSON（face_rectangle）
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try:
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return json.loads(s), True
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except ValueError:
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skipped.append(path + ' [行内JSON解析失败]')
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return None, False
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if s.startswith('['):
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if s == '[]':
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return [], True
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inner = s[1:-1].strip() if s.endswith(']') else ''
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if inner and NUMLIST_RE.match(inner): # 纯数值数组（confidence 列表）
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out = []
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for tok in inner.split(','):
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tok = tok.strip()
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if tok == '':
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continue
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out.append(float(tok) if ('.' in tok or 'e' in tok or 'E' in tok) else int(tok))
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return out, True
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skipped.append(path + ' ' + s[:40]) # [N组坐标]/[N项]/[base64图,…]/[深层结构省略]
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return None, False
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m = MS_RE.match(s) # time_used 的「751 ms」
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if m:
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return float(m.group(1)) if '.' in m.group(1) else int(m.group(1)), True
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if INT_RE.match(s):
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return int(s), True
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if FLOAT_RE.match(s):
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return float(s), True
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return s, True # 字符串（request_id 等）
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def set_path(root, path, value):
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segs = path.split('.')
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cur = root
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for i, seg in enumerate(segs[:-1]):
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nxt = segs[i + 1]
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if seg.isdigit(): # 数字段：上级应是 list（本数据仅 skin_type.details.N，均被摘要跳过）
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idx = int(seg)
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while len(cur) <= idx:
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cur.append({} if not nxt.isdigit() else [])
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if cur[idx] is None:
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cur[idx] = [] if nxt.isdigit() else {}
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cur = cur[idx]
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else:
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if seg not in cur or not isinstance(cur[seg], (dict, list)):
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cur[seg] = [] if nxt.isdigit() else {}
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cur = cur[seg]
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last = segs[-1]
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if last.isdigit():
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idx = int(last)
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while len(cur) <= idx:
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cur.append(None)
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cur[idx] = value
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else:
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cur[last] = value
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def build_case2(flat_text):
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doc = {}
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section = None # 当前 —— section —— 名；None=顶层
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for line in flat_text.split('\n'):
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line = line.strip()
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if not line:
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continue
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m = SEC_RE.match(line)
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if m:
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section = m.group(1)
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continue
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m = KV_RE.match(line)
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if not m:
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continue
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path, raw_v = m.group(1).strip(), m.group(2)
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full_path = (section + '.' + path) if section else path
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value, ok = parse_value(full_path, raw_v)
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if not ok:
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continue
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target = doc
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if section:
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if section not in doc or not isinstance(doc[section], dict):
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doc[section] = {}
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target = doc[section]
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set_path(target, path, value)
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fixture = {
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'request_id': doc.get('request_id'),
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'time_used': doc.get('time_used'),
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'result': slim(doc.get('result', {})),
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'face_rectangle': doc.get('face_rectangle'),
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'side': {
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'left': slim(doc.get('left_side_result')),
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'right': slim(doc.get('right_side_result'))
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}
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}
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return fixture
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# ─── 真值表：Excel 第 3/4 行 → truth.json ───
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def col(ws, row, letter):
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return ws['%s%d' % (letter, row)].value
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def build_truth(ws, row):
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def cat(p_col, s_col, tier_col, copy_col):
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return {
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'P': col(ws, row, p_col), 'S': col(ws, row, s_col),
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'tier': col(ws, row, tier_col), 'copy': col(ws, row, copy_col)
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}
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def dim(p_col, s_col, tier_col):
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return {'P': col(ws, row, p_col), 'S': col(ws, row, s_col), 'tier': col(ws, row, tier_col)}
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return {
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'profile': {'realAge': col(ws, row, 'AS'), 'skinType': '混合性'}, # 综合文案实测输出「混合性」
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'skinAge': col(ws, row, 'E'),
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'totalScore': col(ws, row, 'F'),
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'cats': {
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'contour': cat('I', 'J', 'K', 'L'),
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'wrinkles': cat('M', 'N', 'O', 'P'),
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'smooth': cat('Q', 'R', 'S', 'T'),
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'tone': cat('U', 'V', 'W', 'X'),
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'sens': cat('Y', 'Z', 'AA', 'AB'),
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'acne': cat('AC', 'AD', 'AE', 'AF')
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},
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'dims': {
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'firm': dim('AG', 'AH', 'AI'),
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'vita': dim('AJ', 'AK', 'AL'),
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'stab': dim('AM', 'AN', 'AO')
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},
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'jawline': {'left': col(ws, row, 'AP'), 'right': col(ws, row, 'AQ')},
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'group': col(ws, row, 'AT'),
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'essence': col(ws, row, 'AU'),
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'avgS': col(ws, row, 'AV'),
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'summary': col(ws, row, 'AW'),
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'lifestyle': col(ws, row, 'AX')
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}
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def dump(name, obj):
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path = os.path.join(OUT_DIR, name)
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with open(path, 'w', encoding='utf-8') as f:
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json.dump(obj, f, ensure_ascii=False, indent=1)
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print(' 写出 %s（%d 字节）' % (os.path.relpath(path, ROOT), os.path.getsize(path)))
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def main():
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os.makedirs(OUT_DIR, exist_ok=True)
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print('== 案例1 fixture ← ref_3view_raw.json ==')
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dump('case1.json', build_case1())
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print('== 案例2 fixture ← Excel AY4 扁平化解析 ==')
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wb = openpyxl.load_workbook(XLSX, data_only=True)
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ws = wb['三视图检测汇总']
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case2 = build_case2(ws['AY4'].value)
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dump('case2.json', case2)
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print(' [摘要] 跳过 %d 个路径（坐标数组/base64/深层结构，评分只用标量，不影响）：' % len(skipped))
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for s in skipped:
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print(' - ' + s)
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print('== 真值表 ← Excel 第 3/4 行 ==')
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dump('truth.json', {'case1': build_truth(ws, 3), 'case2': build_truth(ws, 4)})
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# 构建期自检：fixture 关键标量 vs 真值
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c1 = json.load(open(os.path.join(OUT_DIR, 'case1.json'), encoding='utf-8'))
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c2 = json.load(open(os.path.join(OUT_DIR, 'case2.json'), encoding='utf-8'))
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t = json.load(open(os.path.join(OUT_DIR, 'truth.json'), encoding='utf-8'))
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for tag, fx, tr in (('案例1', c1, t['case1']), ('案例2', c2, t['case2'])):
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la = fx['side']['left']['left_jawline_info']['left_jawline_angle']
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ra = fx['side']['right']['right_jawline_info']['right_jawline_angle']
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sa = fx['result']['skin_age']['value']
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assert abs(la - tr['jawline']['left']) < 1e-9, (tag, '左下颌角', la, tr['jawline']['left'])
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assert abs(ra - tr['jawline']['right']) < 1e-9, (tag, '右下颌角', ra, tr['jawline']['right'])
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assert sa == tr['skinAge'], (tag, '肤龄', sa, tr['skinAge'])
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print(' %s 自检通过：下颌角 %.2f/%.2f、肤龄 %d 与真值一致' % (tag, la, ra, sa))
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if __name__ == '__main__':
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sys.exit(main())
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Vendored
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File diff suppressed because it is too large
Load Diff
Vendored
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{
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"request_id": "1790403132,0f99111b-571a-46f5-8540-8209c21b3755",
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"time_used": 751,
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"result": {
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"skin_age": {
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"value": 50
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},
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"eye_pouch": {
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"value": 0,
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"confidence": 0.9219674
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},
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"dark_circle": {
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"value": 3,
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"confidence": 1
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},
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"dark_circle_severity": {
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"value": 0,
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"confidence": 1
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},
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"forehead_wrinkle": {
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"value": 1,
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"confidence": 0.57314694
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},
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"crows_feet": {
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"value": 0,
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"confidence": 0.6948663
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},
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"eye_finelines": {
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"value": 0,
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"confidence": 0.5346259
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},
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"glabella_wrinkle": {
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"value": 0,
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"confidence": 0.65657294
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},
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"nasolabial_fold": {
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"value": 1,
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"confidence": 0.80668426
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},
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"nasolabial_fold_severity": {
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"value": 0,
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"confidence": 0.3970088
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},
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"skin_type": {
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"skin_type": 3
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},
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"pores_forehead": {
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"value": 0,
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"confidence": 1
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},
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"pores_left_cheek": {
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"value": 0,
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"confidence": 1
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},
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"pores_right_cheek": {
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"value": 0,
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"confidence": 1
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},
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"pores_jaw": {
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"value": 0,
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"confidence": 1
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},
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"blackhead": {
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"value": 0,
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"confidence": 1
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},
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"skintone_ita": {
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"ITA": 29.471874,
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"skintone": 3
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},
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"skin_hue_ha": {
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"HA": 57.833736,
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"skin_hue": 0
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},
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"sensitivity": {
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"sensitivity_area": 0,
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"sensitivity_intensity": 0
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},
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"acne": {
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"rectangle": [],
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"confidence": [],
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"polygon": [],
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"count": 0
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},
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"mole": {
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"confidence": [
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0.3529357
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],
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"count": 1
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},
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"brown_spot": {
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"count": 23
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},
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"closed_comedones": {
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"rectangle": [],
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"confidence": [],
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"polygon": [],
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"count": 0
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},
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"acne_mark": {
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"rectangle": [],
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"confidence": [],
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"polygon": [],
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"count": 0
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},
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"acne_nodule": {
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"rectangle": [],
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"confidence": [],
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"polygon": [],
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"count": 0
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},
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"acne_pustule": {
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"rectangle": [],
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"confidence": [],
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"polygon": [],
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"count": 0
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},
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"blackhead_count": 3,
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"skintone": {
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"value": 2,
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"confidence": 0.7146322
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},
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"fine_line": {
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"forehead_count": 1,
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"left_undereye_count": 3,
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"right_undereye_count": 3,
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"left_cheek_count": 0,
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"right_cheek_count": 0,
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"left_crowsfeet_count": 1,
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"right_crowsfeet_count": 0,
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"glabella_count": 0
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},
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"wrinkle_count": {
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"forehead_count": 1,
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"left_undereye_count": 2,
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"right_undereye_count": 3,
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"left_mouth_count": 0,
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"right_mouth_count": 0,
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"left_nasolabial_count": 1,
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"right_nasolabial_count": 1,
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"glabella_count": 0,
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"left_cheek_count": 0,
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"right_cheek_count": 0,
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"left_crowsfeet_count": 0,
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"right_crowsfeet_count": 0
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
"area": 0.16,
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||||
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|
||||
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||||
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|
||||
"area": 0,
|
||||
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|
||||
},
|
||||
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|
||||
"area": 0.49,
|
||||
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||||
}
|
||||
},
|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
}
|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"area": 0.007
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
"value": 0
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||||
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||||
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||||
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||||
},
|
||||
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|
||||
"value": 0
|
||||
},
|
||||
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||||
"value": 1
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||||
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||||
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|
||||
"value": 0
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||||
},
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||||
"left_eye_finelines_severity": {
|
||||
"value": 0
|
||||
},
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||||
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|
||||
"value": 0
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||||
},
|
||||
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||||
"value": 0
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||||
},
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||||
"left_nasolabial_fold_severity": {
|
||||
"value": 2
|
||||
},
|
||||
"right_nasolabial_fold_severity": {
|
||||
"value": 2
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||||
},
|
||||
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|
||||
"value": 0
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||||
},
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||||
"right_cheek_wrinkle_severity": {
|
||||
"value": 0
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||||
},
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||||
"forehead_wrinkle_info": {
|
||||
"wrinkle_score": 5,
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||||
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||||
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||||
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||||
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||||
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||||
"wrinkle_deep_ratio": 0.6260869565217392,
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||||
"wrinkle_deep_num": 1,
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||||
"wrinkle_shallow_num": 1
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||||
},
|
||||
"left_eye_wrinkle_info": {
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||||
"wrinkle_score": 6,
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||||
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||||
"wrinkle_norm_length": 1.3718454398776636,
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||||
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||||
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||||
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||||
"wrinkle_deep_ratio": 0.35384615384615387,
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||||
"wrinkle_deep_num": 2,
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||||
"wrinkle_shallow_num": 3
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||||
},
|
||||
"right_eye_wrinkle_info": {
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||||
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||||
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||||
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||||
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||||
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||||
"wrinkle_area_ratio": 0.08267090620031796,
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||||
"wrinkle_deep_ratio": 0.5783132530120482,
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||||
"wrinkle_deep_num": 3,
|
||||
"wrinkle_shallow_num": 3
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||||
},
|
||||
"left_crowsfeet_wrinkle_info": {
|
||||
"wrinkle_score": 10,
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||||
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|
||||
"wrinkle_norm_length": 0.46431691811243997,
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||||
"wrinkle_norm_depth": 0.11122994652406416,
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||||
"wrinkle_pixel_density": 0.42449501019699687,
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||||
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||||
"wrinkle_deep_ratio": 0,
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||||
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|
||||
"wrinkle_shallow_num": 1
|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
"wrinkle_norm_length": 0,
|
||||
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|
||||
"wrinkle_pixel_density": 0,
|
||||
"wrinkle_area_ratio": 0,
|
||||
"wrinkle_deep_ratio": 0,
|
||||
"wrinkle_deep_num": 0,
|
||||
"wrinkle_shallow_num": 0
|
||||
},
|
||||
"glabella_wrinkle_info": {
|
||||
"wrinkle_score": 0,
|
||||
"wrinkle_severity_level": 0,
|
||||
"wrinkle_norm_length": 0,
|
||||
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|
||||
"wrinkle_pixel_density": 0,
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||||
"wrinkle_area_ratio": 0,
|
||||
"wrinkle_deep_ratio": 0,
|
||||
"wrinkle_deep_num": 0,
|
||||
"wrinkle_shallow_num": 0
|
||||
},
|
||||
"left_mouth_wrinkle_info": {
|
||||
"wrinkle_score": 0,
|
||||
"wrinkle_severity_level": 0,
|
||||
"wrinkle_norm_length": 0,
|
||||
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|
||||
"wrinkle_pixel_density": 0,
|
||||
"wrinkle_area_ratio": 0,
|
||||
"wrinkle_deep_ratio": 0,
|
||||
"wrinkle_deep_num": 0,
|
||||
"wrinkle_shallow_num": 0
|
||||
},
|
||||
"right_mouth_wrinkle_info": {
|
||||
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|
||||
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|
||||
"wrinkle_norm_length": 0,
|
||||
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|
||||
"wrinkle_pixel_density": 0,
|
||||
"wrinkle_area_ratio": 0,
|
||||
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|
||||
"wrinkle_deep_num": 0,
|
||||
"wrinkle_shallow_num": 0
|
||||
},
|
||||
"left_nasolabial_wrinkle_info": {
|
||||
"wrinkle_score": 36,
|
||||
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|
||||
"wrinkle_norm_length": 0.23676013879409538,
|
||||
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||||
"wrinkle_pixel_density": 0.06861427532071777,
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||||
"wrinkle_area_ratio": 0.09025069637883008,
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||||
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|
||||
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|
||||
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|
||||
},
|
||||
"right_nasolabial_wrinkle_info": {
|
||||
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|
||||
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|
||||
"wrinkle_norm_length": 0.22799124476468444,
|
||||
"wrinkle_norm_depth": 0.49550173010380627,
|
||||
"wrinkle_pixel_density": 0.07321052192999311,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"left_cheek_wrinkle_info": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"right_cheek_wrinkle_info": {
|
||||
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|
||||
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|
||||
"wrinkle_norm_length": 0,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"score_info": {
|
||||
"dark_circle_score": 89,
|
||||
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|
||||
"wrinkle_score": 90,
|
||||
"oily_intensity_score": 38,
|
||||
"pores_score": 97,
|
||||
"blackhead_score": 99,
|
||||
"acne_score": 100,
|
||||
"sensitivity_score": 100,
|
||||
"melanin_score": 100,
|
||||
"water_score": 93,
|
||||
"rough_score": 84,
|
||||
"total_score": 88,
|
||||
"pores_type_score": {
|
||||
"pores_forehead_score": 92,
|
||||
"pores_leftcheek_score": 98,
|
||||
"pores_rightcheek_score": 98,
|
||||
"pores_jaw_score": 100
|
||||
},
|
||||
"dark_circle_type_score": {
|
||||
"left_dark_circle_score": 89,
|
||||
"right_dark_circle_score": 100
|
||||
},
|
||||
"red_spot_score": 97
|
||||
},
|
||||
"left_eye_pouch_rect": {
|
||||
"left": 102,
|
||||
"top": 137,
|
||||
"width": 68,
|
||||
"height": 59
|
||||
},
|
||||
"right_eye_pouch_rect": {
|
||||
"left": 214,
|
||||
"top": 136,
|
||||
"width": 67,
|
||||
"height": 59
|
||||
},
|
||||
"melasma": {
|
||||
"value": 1,
|
||||
"confidence": 0.50907385
|
||||
},
|
||||
"freckle": {
|
||||
"value": 0,
|
||||
"confidence": 0.45538568
|
||||
},
|
||||
"image_quality": {
|
||||
"face_rect": {
|
||||
"left": 81,
|
||||
"top": 29,
|
||||
"width": 229,
|
||||
"height": 320
|
||||
},
|
||||
"face_ratio": 0.42792428,
|
||||
"hair_occlusion": 0.11386067,
|
||||
"face_orientation": {
|
||||
"yaw": -5.9413967,
|
||||
"pitch": 6.6220365,
|
||||
"roll": 2.846047
|
||||
},
|
||||
"glasses": 0
|
||||
},
|
||||
"sensitivity_type_v1": 1
|
||||
},
|
||||
"face_rectangle": {
|
||||
"top": 108,
|
||||
"left": 77,
|
||||
"width": 238,
|
||||
"height": 238
|
||||
},
|
||||
"side": {
|
||||
"left": {
|
||||
"left_jawline_info": {
|
||||
"left_jawline_quality": 1,
|
||||
"left_jawline_angle_level": 1,
|
||||
"left_jawline_angle": 114.81
|
||||
},
|
||||
"left_acne_info": {
|
||||
"score": 98,
|
||||
"acne": {
|
||||
"rectangle": [],
|
||||
"confidence": [],
|
||||
"polygon": [],
|
||||
"count": 0
|
||||
},
|
||||
"acne_pustule": {
|
||||
"rectangle": [],
|
||||
"confidence": [],
|
||||
"polygon": [],
|
||||
"count": 0
|
||||
},
|
||||
"acne_nodule": {
|
||||
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|
||||
"confidence": [],
|
||||
"polygon": [],
|
||||
"count": 0
|
||||
},
|
||||
"acne_mark": {
|
||||
"rectangle": [],
|
||||
"confidence": [],
|
||||
"polygon": [],
|
||||
"count": 0
|
||||
},
|
||||
"closed_comedones": {
|
||||
"confidence": [
|
||||
0.33919
|
||||
],
|
||||
"count": 1
|
||||
}
|
||||
},
|
||||
"left_melanin_info": {
|
||||
"mole": {
|
||||
"confidence": [
|
||||
0.35832882
|
||||
],
|
||||
"count": 1
|
||||
},
|
||||
"brown_spot": {
|
||||
"confidence": [
|
||||
0.4598653,
|
||||
0.40733993,
|
||||
0.3679863,
|
||||
0.35319555,
|
||||
0.3008949
|
||||
],
|
||||
"count": 5
|
||||
}
|
||||
}
|
||||
},
|
||||
"right": {
|
||||
"right_jawline_info": {
|
||||
"right_jawline_quality": 1,
|
||||
"right_jawline_angle_level": 1,
|
||||
"right_jawline_angle": 103.95
|
||||
},
|
||||
"right_acne_info": {
|
||||
"score": 100,
|
||||
"acne": {
|
||||
"rectangle": [],
|
||||
"confidence": [],
|
||||
"polygon": [],
|
||||
"count": 0
|
||||
},
|
||||
"acne_pustule": {
|
||||
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|
||||
"confidence": [],
|
||||
"polygon": [],
|
||||
"count": 0
|
||||
},
|
||||
"acne_nodule": {
|
||||
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|
||||
"confidence": [],
|
||||
"polygon": [],
|
||||
"count": 0
|
||||
},
|
||||
"acne_mark": {
|
||||
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|
||||
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|
||||
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|
||||
"count": 0
|
||||
},
|
||||
"closed_comedones": {
|
||||
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|
||||
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|
||||
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|
||||
"count": 0
|
||||
}
|
||||
},
|
||||
"right_melanin_info": {
|
||||
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|
||||
"confidence": [
|
||||
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|
||||
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|
||||
],
|
||||
"count": 2
|
||||
},
|
||||
"brown_spot": {
|
||||
"count": 8
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
Vendored
+146
@@ -0,0 +1,146 @@
|
||||
{
|
||||
"case1": {
|
||||
"profile": {
|
||||
"realAge": 30,
|
||||
"skinType": "混合性"
|
||||
},
|
||||
"skinAge": 27,
|
||||
"totalScore": 70,
|
||||
"cats": {
|
||||
"contour": {
|
||||
"P": 75.5,
|
||||
"S": 9.27,
|
||||
"tier": "预警档",
|
||||
"copy": "下颌线角度左104.54°/右103.75°（标准区间116–120°，双侧均偏离标准，提示下颌缘支撑力下降）；眼袋1级、黑眼圈1级；中面部支撑与轮廓清晰度处于预警水平。建议加强轮廓紧致护理：重点针对下颌线与下颌缘区域使用紧致类精华，配合由下向上的提拉按摩手法，同时加强眼周支撑护理，将紧致防线前置。"
|
||||
},
|
||||
"wrinkles": {
|
||||
"P": 23,
|
||||
"S": 7.69,
|
||||
"tier": "预警档",
|
||||
"copy": "全脸皱纹159条、细纹150条，最差区域为左嘴角纹（分区分77，最高严重度3级），部分区域已现静态纹趋势。建议针对眼周、法令纹等高发区使用视黄醇/胜肽类淡纹精华，遏制静态纹转化。"
|
||||
},
|
||||
"smooth": {
|
||||
"P": 57.2,
|
||||
"S": 8.72,
|
||||
"tier": "预警档",
|
||||
"copy": "皮肤表面平整度较低，最差毛孔区位于额头（分区毛孔分32），粗大毛孔1041颗、黑头113颗，T区出油面积57%、左右脸颊20%/2%，纹理分仅82，整体细腻度不足。建议避免过度清洁，重点关注额头，配合低浓度酸类周期护理与柔润精华。"
|
||||
},
|
||||
"tone": {
|
||||
"P": 85,
|
||||
"S": 9.55,
|
||||
"tier": "改善档",
|
||||
"copy": "肤色ITA 58.6（极白，HA 56.2），色素分92，整体稳定但存在局部暗沉倾向（未见明显色斑标记，检出褐色斑37处）；水分分78，缺水面积19.8%、严重度22。建议加强抗氧化与光泽管理，做好基础防晒与补水。"
|
||||
},
|
||||
"sens": {
|
||||
"P": 99,
|
||||
"S": 9.97,
|
||||
"tier": "良好档",
|
||||
"copy": "泛红面积0.6%，红区分99，屏障状态稳定，未见明显敏感信号。建议维持现有温和护理，避免过度去角质。"
|
||||
},
|
||||
"acne": {
|
||||
"P": 70,
|
||||
"S": 9.1,
|
||||
"tier": "预警档",
|
||||
"copy": "检出9处皮损（闭口粉刺6处、丘疹3处）。建议尽快进行针对性抗痘护理，避免挤压与刺激，必要时寻求皮肤科专业处理。"
|
||||
}
|
||||
},
|
||||
"dims": {
|
||||
"firm": {
|
||||
"P": 23,
|
||||
"S": 7.69,
|
||||
"tier": "预警档"
|
||||
},
|
||||
"vita": {
|
||||
"P": 57.2,
|
||||
"S": 8.72,
|
||||
"tier": "预警档"
|
||||
},
|
||||
"stab": {
|
||||
"P": 70,
|
||||
"S": 9.1,
|
||||
"tier": "预警档"
|
||||
}
|
||||
},
|
||||
"jawline": {
|
||||
"left": 104.54,
|
||||
"right": 103.75
|
||||
},
|
||||
"group": "25+组",
|
||||
"essence": "35+支撑精华",
|
||||
"avgS": 9.05,
|
||||
"summary": "【综合】实际年龄30岁（接口测得肌肤年龄27岁，差3岁），产品组别25+组；六分类S均分9.05、最差分类皱纹 S=7.69。按三重判据（触发：最差项约束）推荐35+支撑精华：优先改善皱纹（P=23.0）与光滑（P=57.2），其余维度维持现有护理节奏。整体肤质混合性，T区出油面积57%、左右脸颊20%/2%。",
|
||||
"lifestyle": "保持侧卧改仰卧的睡姿习惯，减少枕巾摩擦；控制屏幕时间，缓解眼周疲劳"
|
||||
},
|
||||
"case2": {
|
||||
"profile": {
|
||||
"realAge": 47,
|
||||
"skinType": "混合性"
|
||||
},
|
||||
"skinAge": 50,
|
||||
"totalScore": 88,
|
||||
"cats": {
|
||||
"contour": {
|
||||
"P": 75.9,
|
||||
"S": 9.28,
|
||||
"tier": "预警档",
|
||||
"copy": "下颌线角度左114.81°/右103.95°（标准区间116–120°，双侧均偏离标准，提示下颌缘支撑力下降）；眼袋0级、黑眼圈0级；中面部支撑与轮廓清晰度处于预警水平。建议加强轮廓紧致护理：重点针对下颌线与下颌缘区域使用紧致类精华，配合由下向上的提拉按摩手法，同时加强眼周支撑护理，将紧致防线前置。"
|
||||
},
|
||||
"wrinkles": {
|
||||
"P": 50,
|
||||
"S": 8.5,
|
||||
"tier": "预警档",
|
||||
"copy": "全脸皱纹8条、细纹8条，最差区域为右法令纹（分区分50，最高严重度2级），部分区域已现静态纹趋势。建议针对眼周、法令纹等高发区使用视黄醇/胜肽类淡纹精华，遏制静态纹转化。"
|
||||
},
|
||||
"smooth": {
|
||||
"P": 91.7,
|
||||
"S": 9.75,
|
||||
"tier": "良好档",
|
||||
"copy": "皮肤表面细腻平滑，最差毛孔区额头控制良好（分区毛孔分92，粗大毛孔100颗、黑头3颗），纹理分84，角质层排列整齐。建议维持温和清洁与保湿节奏。"
|
||||
},
|
||||
"tone": {
|
||||
"P": 91.5,
|
||||
"S": 9.75,
|
||||
"tier": "良好档",
|
||||
"copy": "肤色ITA 29.5（小麦色，HA 57.8），色素分100，肤色均匀透亮（黄褐斑，检出褐色斑23处、痣1处）；水分分93，缺水面积6.8%、严重度7。色素与水润状态均衡，建议持续做好防晒与基础保湿。"
|
||||
},
|
||||
"sens": {
|
||||
"P": 98.7,
|
||||
"S": 9.96,
|
||||
"tier": "良好档",
|
||||
"copy": "泛红面积0.9%，红区分100，屏障状态稳定，未见明显敏感信号。建议维持现有温和护理，避免过度去角质。"
|
||||
},
|
||||
"acne": {
|
||||
"P": 100,
|
||||
"S": 10,
|
||||
"tier": "良好档",
|
||||
"copy": "未检出痤疮与皮损。建议维持清洁与控油节奏，注意作息与饮食。"
|
||||
}
|
||||
},
|
||||
"dims": {
|
||||
"firm": {
|
||||
"P": 50,
|
||||
"S": 8.5,
|
||||
"tier": "预警档"
|
||||
},
|
||||
"vita": {
|
||||
"P": 91.5,
|
||||
"S": 9.75,
|
||||
"tier": "良好档"
|
||||
},
|
||||
"stab": {
|
||||
"P": 98.7,
|
||||
"S": 9.96,
|
||||
"tier": "良好档"
|
||||
}
|
||||
},
|
||||
"jawline": {
|
||||
"left": 114.81,
|
||||
"right": 103.95
|
||||
},
|
||||
"group": "50+组",
|
||||
"essence": "50+重塑精华",
|
||||
"avgS": 9.54,
|
||||
"summary": "【综合】实际年龄47岁（接口测得肌肤年龄50岁，差3岁），产品组别50+组；六分类S均分9.54、最差分类皱纹 S=8.50。按三重判据（触发：年龄组档）推荐50+重塑精华：优先改善皱纹（P=50.0）与轮廓（P=75.9），其余维度维持现有护理节奏。整体肤质混合性，T区出油面积62%、左右脸颊16%/16%。",
|
||||
"lifestyle": "保持侧卧改仰卧的睡姿习惯，减少枕巾摩擦；控制屏幕时间，缓解眼周疲劳"
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,128 @@
|
||||
/* ═════ 巨子测肤 H5 — 评分引擎真值回归（Excel 三视图表 × SkinScore）═════
|
||||
* 用法：node h5/test/regression.js
|
||||
* fixture 重建：python3 h5/test/build_fixtures.py（Excel 或参考 JSON 变更后需重建）
|
||||
*
|
||||
* 断言目标：docs/皮肤检测测试结果_三视图_20260926.xlsx「三视图检测汇总」第 3/4 行
|
||||
* - 六分类 P(I/M/Q/U/Y/AC)、S(J/N/R/V/Z/AD)、档位(K/O/S/W/AA/AE)
|
||||
* - 三维度 P/S/档位(AG..AO)、下颌线角度(AP/AQ)、产品组别(AT)、精华线(AU)、均分(AV)
|
||||
* - 6 分类文案(L/P/T/X/AB/AF)、综合文案(AW)、生活方式(AX) 逐字一致
|
||||
* - 浮点容差 0.05；文案逐字无容差
|
||||
*
|
||||
* 引擎与真值不一致时：不改 h5/js/skin-score.js（审计代理负责），差异登记在 KNOWN_DIFFS
|
||||
* 并输出明细；未登记的新差异 → 退出码 1。登记项若转为通过（引擎已修复）会提示清理。
|
||||
*/
|
||||
'use strict';
|
||||
|
||||
var fs = require('fs');
|
||||
var path = require('path');
|
||||
var SkinScore = require(path.join(__dirname, '..', 'js', 'skin-score.js'));
|
||||
|
||||
/* ─── 已知差异登记：键 = 案例.列，值 = 差异原因（逐条说明）。当前为空：V2.2 审计后
|
||||
引擎与两组真值全部一致；今后出现引擎/真值不一致时在此登记，未登记的新差异 → 退出码 1 ─── */
|
||||
var KNOWN_DIFFS = {};
|
||||
|
||||
/* ─── 断言计数与输出 ─── */
|
||||
var TOL = 0.05 + 1e-6; // 浮点容差 0.05（1e-6 仅抵二进制表示误差，如 98.65 vs 98.7）
|
||||
var pass = 0, known = 0, fail = 0;
|
||||
var failLines = [], knownLines = [];
|
||||
|
||||
function diffCtx(exp, act) { // 首个分歧点 ±30 字上下文
|
||||
var i = 0, n = Math.min(exp.length, act.length);
|
||||
while (i < n && exp[i] === act[i]) i++;
|
||||
var lo = Math.max(0, i - 30);
|
||||
return ' 分歧@' + i + '：\n 真值 …' + exp.slice(lo, i + 30) + '…\n 引擎 …' + act.slice(lo, i + 30) + '…';
|
||||
}
|
||||
function okNum(label, actual, expected) {
|
||||
if (typeof actual === 'number' && typeof expected === 'number' && Math.abs(actual - expected) <= TOL) {
|
||||
pass++; console.log(' ✓ ' + label + ' = ' + actual + (actual !== expected ? '（真值 ' + expected + '，容差内）' : ''));
|
||||
} else {
|
||||
fail++; failLines.push(label + '：引擎 ' + actual + ' ≠ 真值 ' + expected);
|
||||
console.error(' ✗ ' + label + '：引擎 ' + actual + ' ≠ 真值 ' + expected);
|
||||
}
|
||||
}
|
||||
function okStr(label, actual, expected, knownKey) {
|
||||
if (actual === expected) { pass++; console.log(' ✓ ' + label); return; }
|
||||
var reasons = knownKey ? KNOWN_DIFFS[knownKey] : null;
|
||||
if (reasons) { // 已登记：已知差异，输出明细，不计失败
|
||||
known++;
|
||||
knownLines.push(knownKey);
|
||||
console.log(' Δ ' + label + ' 已知差异（' + reasons.length + ' 条原因）：');
|
||||
for (var i = 0; i < reasons.length; i++) console.log(' 原因' + (i + 1) + '：' + reasons[i]);
|
||||
console.log(diffCtx(String(expected), String(actual)));
|
||||
} else {
|
||||
fail++;
|
||||
failLines.push(label + '：文案不一致\n' + diffCtx(String(expected), String(actual)));
|
||||
console.error(' ✗ ' + label + '：文案不一致（未登记差异）\n' + diffCtx(String(expected), String(actual)));
|
||||
}
|
||||
}
|
||||
|
||||
/* ─── 列号映射：分类(文案列)与维度 ─── */
|
||||
var CAT_COLS = { contour: 'I/J/K/L', wrinkles: 'M/N/O/P', smooth: 'Q/R/S/T', tone: 'U/V/W/X', sens: 'Y/Z/AA/AB', acne: 'AC/AD/AE/AF' };
|
||||
var CAT_COPY_COL = { contour: 'L', wrinkles: 'P', smooth: 'T', tone: 'X', sens: 'AB', acne: 'AF' };
|
||||
var DIM_COLS = { firm: 'AG/AH/AI', vita: 'AJ/AK/AL', stab: 'AM/AN/AO' };
|
||||
|
||||
/* ─── 主流程 ─── */
|
||||
function loadJSON(name) {
|
||||
return JSON.parse(fs.readFileSync(path.join(__dirname, 'fixtures', name), 'utf8'));
|
||||
}
|
||||
|
||||
function main() {
|
||||
var truth = loadJSON('truth.json');
|
||||
var cases = [
|
||||
{ tag: 'case1', fixture: loadJSON('case1.json'), truth: truth.case1 },
|
||||
{ tag: 'case2', fixture: loadJSON('case2.json'), truth: truth.case2 }
|
||||
];
|
||||
|
||||
for (var ci = 0; ci < cases.length; ci++) {
|
||||
var tag = cases[ci].tag, fixture = cases[ci].fixture, tr = cases[ci].truth;
|
||||
console.log('== ' + tag + '（realAge=' + tr.profile.realAge + '，skinType=' + tr.profile.skinType + '） ==');
|
||||
var vm = SkinScore.buildViewModel(fixture.result, fixture.side, tr.profile);
|
||||
|
||||
// 下颌线角度 AP/AQ：fixture 保真校验（引擎消费角度后由 L 列逐字断言兜底）
|
||||
okNum(tag + '.AP 左下颌线角度', fixture.side.left.left_jawline_info.left_jawline_angle, tr.jawline.left);
|
||||
okNum(tag + '.AQ 右下颌线角度', fixture.side.right.right_jawline_info.right_jawline_angle, tr.jawline.right);
|
||||
okNum(tag + '.E 肌肤年龄', vm.skinAgeRaw, tr.skinAge);
|
||||
okNum(tag + '.F 总分', vm.total, tr.totalScore);
|
||||
|
||||
// 六分类 P/S/档位 + 文案逐字
|
||||
for (var k in CAT_COLS) {
|
||||
var cols = CAT_COLS[k].split('/'), c = vm.cats[k], t = tr.cats[k];
|
||||
okNum(tag + '.' + cols[0] + ' ' + c.name + '·P', c.P, t.P);
|
||||
okNum(tag + '.' + cols[1] + ' ' + c.name + '·S', c.S, t.S);
|
||||
okStr(tag + '.' + cols[2] + ' ' + c.name + '·档位', c.level ? c.level + '档' : String(c.level), t.tier);
|
||||
okStr(tag + '.' + CAT_COPY_COL[k] + ' ' + c.name + '·文案', c.text, t.copy, tag + '.' + CAT_COPY_COL[k]);
|
||||
}
|
||||
// 三维度 P/S/档位
|
||||
for (var d in DIM_COLS) {
|
||||
var dcols = DIM_COLS[d].split('/'), dd = vm.dims[d], dt = tr.dims[d];
|
||||
okNum(tag + '.' + dcols[0] + ' ' + dd.name + '·P', dd.P, dt.P);
|
||||
okNum(tag + '.' + dcols[1] + ' ' + dd.name + '·S', dd.S, dt.S);
|
||||
okStr(tag + '.' + dcols[2] + ' ' + dd.name + '·档位', dd.level ? dd.level + '档' : String(dd.level), dt.tier);
|
||||
}
|
||||
// 组别/精华/均分/综合文案/生活方式
|
||||
okStr(tag + '.AT 产品组别', String(vm.group), tr.group);
|
||||
okStr(tag + '.AU 精华产品线', vm.essence.name, tr.essence);
|
||||
okNum(tag + '.AV 六分类S均分', vm.avgS, tr.avgS);
|
||||
okStr(tag + '.AW 综合文案', vm.summary, tr.summary, tag + '.AW');
|
||||
okStr(tag + '.AX 生活方式', vm.lifestyle, tr.lifestyle);
|
||||
}
|
||||
|
||||
// 登记项清理提示：已知差异键本轮全部命中为差异；若某键实际通过则提示移除登记
|
||||
var knownHit = {}, i;
|
||||
for (i = 0; i < knownLines.length; i++) knownHit[knownLines[i]] = 1;
|
||||
for (var key in KNOWN_DIFFS) {
|
||||
if (!knownHit[key]) console.log(' ◆ 提示：登记项 ' + key + ' 本轮通过（引擎或真值已变化），可从 KNOWN_DIFFS 移除');
|
||||
}
|
||||
|
||||
console.log('----------------------------------------');
|
||||
console.log('断言 ' + (pass + known + fail) + ' 条：通过 ' + pass + '，已知差异 ' + known + '，失败 ' + fail);
|
||||
if (knownLines.length) console.log('已知差异明细：' + knownLines.join('、') + '（原因见上，待 skin-score.js 审计代理处理）');
|
||||
if (failLines.length) {
|
||||
console.error('失败明细：');
|
||||
for (i = 0; i < failLines.length; i++) console.error(' ✗ ' + failLines[i]);
|
||||
}
|
||||
if (fail > 0) process.exit(1);
|
||||
console.log(known > 0 ? '结论：全绿（仅剩已登记、已说明原因的已知差异）' : '结论：全绿');
|
||||
}
|
||||
|
||||
main();
|
||||
Reference in New Issue
Block a user